5. Workshop Responsible Data Science - Discussion on Transparency in data science by Jack van Wijk
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This document discusses the need for transparency in data science and decision making. It notes that decisions made by algorithms are currently like "black boxes" where the reasoning is not understandable. The document suggests that transparency could be achieved by visualizing the data and assumptions used, the reasoning process, and criteria for decisions. However, it also notes that achieving full transparency may not be feasible due to the complexity of data processing pipelines and models like deep learning that are not fully understandable. It concludes by suggesting that better technology, legislation, accountability, market forces, or consumer advocacy could help increase transparency even if full transparency is not possible.
![www.jads.nl
[2]
BLACK
BOX
Decision
- you may also like that book
- you are not illegible for a loan
- you must get an expensive
treatment
- you are fired
- you do not get parole
We need
transparency!](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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5. Workshop Responsible Data Science - Discussion on Transparency in data science by Jack van Wijk
- 1. Transparency Data science that provides transparency – How to clarify answers such that they become indisputable? December 2, 2016 Jack van Wijk
- 2. www.jads.nl [2] BLACK BOX Decision - you may also like that book - you are not illegible for a loan - you must get an expensive treatment - you are fired - you do not get parole We need transparency!
- 3. www.jads.nl [3] Data Model Model definition Decision What data was used? What assumptions are made? What reasoning is used? What criteria are used?
- 4. www.jads.nl BaobabView: Visualization of decision trees (vd Elzen et al., 2011) [4]
- 5. www.jads.nl Rationale visualization (Scheepens et al., 2015) [5]
- 6. www.jads.nl [6] Data Model Model definition Decision What data was used? What assumptions are made? What reasoning is used? What criteria are used? We need transparency! But is this feasible?
- 7. www.jads.nl [7] Data Other Data More Data Cleaning Linking Deep learning SVM Rules Parameters Thresholds Feature selection Correlation Twitter IoT Decision We need transparency! But is this feasible?
- 8. www.jads.nl Transparency cannot be achieved, because • Processing pipelines are too complex • no way we can understand what a deep learning network is doing • Understanding requires too much domain expertise • you can only understand this if you have studied medicine • It blocks innovation • explaining everything is expensive, methods like deep learning cannot be used • It opens the door to fraud • tell the outside world what heuristics and proxies we use? no way! [8]
- 9. www.jads.nl To achieve more transparency, we need A) Better technology? • to explain what complex methods are doing B) Legislation? • enforce that decisions are always clear and transparent C) Make companies accountable? • companies must be able to explain what they do D) Free markets? • such that the most transparent companies will win E) Consumer organizations, press attention? • blame and shame ill-behaving companies and governments [9]
- 10. www.jads.nl [10]